基于免疫疫苗的主汽温系统PID优化整定

PID Parameter Optimization for Main Steam Temperature Control System Based on Immune Vaccine Algorithm

为了达到主汽温系统的优化运行以提高热效率,在自适应遗传算法基础上引入基于免疫原理的免疫记忆细胞和疫苗提取、接种算子的免疫疫苗算法,进行了收敛性证明,将其用于主汽温控制系统的PID优化整定,并将获得的PID参数的控制效果与自适应免疫遗传算法(AIGA)获得的PID参数的控制效果进行了比较.结果表明:自适应免疫疫苗算法(AIVA)的收敛稳定性优于自适应免疫遗传算法,与自适应免疫遗传算法获得的PID参数的控制效果相比,自适应免疫疫苗算法所产生的PID参数的控制效果更好,且阶跃响应的调节时间较短,过渡更平稳,证明了该方法的有效性.

To improve the thermal efficiency of main mune vaccine algorithm （AIVA） was proposed based the immune memory cell, vaccine extraction operator steam temperature control system, an adaptive im on adaptive genetic algorithm （AGA） by introducing and vaccine vaccination operator according to immune principles, of which the convergence was subsequently proved. The algorithm was then used to optimize the PID parameters of main steam temperature control system, and the corresponding results were com pared with that of the adaptive immune genetic algorithm （AIGA）. Results show that the convergence of AIVA is more stable than ALGA, and the control effect on PID parameters generated by AIVA is also bet ter than that by AIGA due to both its shorter adjustment time to step response and its more stable transi tion process, which prove this algorithm to be effective.